mindspore.ops.Conv2DTranspose

class mindspore.ops.Conv2DTranspose(out_channel, kernel_size, pad_mode='valid', pad=0, pad_list=None, mode=1, stride=1, dilation=1, group=1, data_format='NCHW')[源代码]

Compute a 2D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution).

Parameters

  • out_channel (int) – The dimensionality of the output space.

  • kernel_size (Union[int, tuple[int]]) – The size of the convolution window.

  • pad_mode (str) – Modes to fill padding. It could be “valid”, “same”, or “pad”. Default: “valid”.

  • pad (Union[int, tuple[int]]) – The pad value to be filled. Default: 0. If pad is an integer, the paddings of top, bottom, left and right are the same, equal to pad. If pad is a tuple of four integers, the padding of top, bottom, left and right equal to pad[0], pad[1], pad[2], and pad[3] correspondingly.

  • pad_list (Union[str, None]) – The pad list like (top, bottom, left, right). Default: None.

  • mode (int) – Modes for different convolutions. 0 Math convolution, 1 cross-correlation convolution , 2 deconvolution, 3 depthwise convolution. Default: 1.

  • stride (Union[int. tuple[int]]) – The stride to be applied to the convolution filter. Default: 1.

  • dilation (Union[int. tuple[int]]) – Specifies the dilation rate to be used for the dilated convolution. Default: 1.

  • group (int) – Splits input into groups. Default: 1.

  • data_format (str) – The format of input and output data. It should be ‘NHWC’ or ‘NCHW’， default is ‘NCHW’.

Inputs:

  • dout (Tensor) - the gradients with respect to the output of the convolution. The shape conforms to the default data_format \((N, C_{out}, H_{out}, W_{out})\).

  • weight (Tensor) - Set size of kernel is \((K_1, K_2)\), then the shape is \((C_{out}, C_{in}, K_1, K_2)\).

  • input_size (Tensor) - A tuple describes the shape of the input which conforms to the format \((N, C_{in}, H_{in}, W_{in})\).

Outputs:

Tensor, the gradients with respect to the input of convolution. It has the same shape as the input.

Raises

  • TypeError – If kernel_size, stride, pad or dilation is neither an int nor a tuple.

  • TypeError – If out_channel or group is not an int.

  • ValueError – If kernel_size, stride or dilation is less than 1.

  • ValueError – If pad_mode is not one of ‘same’, ‘valid’ or ‘pad’.

  • ValueError – If padding is a tuple whose length is not equal to 4.

  • ValueError – If pad_mode it not equal to ‘pad’ and pad is not equal to (0, 0, 0, 0).

  • ValueError – If data_format is neither ‘NCHW’ nor ‘NHWC’.

Supported Platforms:

Ascend GPU CPU

Examples

>>> dout = Tensor(np.ones([10, 32, 30, 30]), mindspore.float32)
>>> weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)
>>> x = Tensor(np.ones([10, 32, 32, 32]))
>>> conv2d_transpose_input = ops.Conv2DTranspose(out_channel=32, kernel_size=3)
>>> output = conv2d_transpose_input(dout, weight, ops.shape(x))
>>> print(output.shape)
(10, 32, 32, 32)